Deep reinforcement learning in production systems: a systematic literature review

M Panzer, B Bender - International Journal of Production Research, 2022 - Taylor & Francis
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …

[HTML][HTML] Neural agent-based production planning and control: An architectural review

M Panzer, B Bender, N Gronau - Journal of Manufacturing Systems, 2022 - Elsevier
Nowadays, production planning and control must cope with mass customization, increased
fluctuations in demand, and high competition pressures. Despite prevailing market risks …

Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning

S Luo, L Zhang, Y Fan - Computers & Industrial Engineering, 2021 - Elsevier
In modern volatile and complex manufacturing environment, dynamic events such as new
job insertions and machine breakdowns may randomly occur at any time and different …

Opportunistic maintenance scheduling with deep reinforcement learning

A Valet, T Altenmüller, B Waschneck, MC May… - Journal of Manufacturing …, 2022 - Elsevier
The great complexity of advanced manufacturing processes combined with the high
investment costs for manufacturing equipment makes the integration of maintenance …

Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events

H Wang, J Cheng, C Liu, Y Zhang, S Hu, L Chen - Applied Soft Computing, 2022 - Elsevier
The economic benefits for manufacturing companies will be influenced by how it handles
potential dynamic events and performs multi-objective real-time scheduling for existing …

[HTML][HTML] Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward

V Samsonov, KB Hicham, T Meisen - Engineering Applications of Artificial …, 2022 - Elsevier
Abstract The field of Neural Combinatorial Optimization (NCO) offers multiple learning-
based approaches to solve well-known combinatorial optimization tasks such as Traveling …

Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning

Z Müller-Zhang, T Kuhn, PO Antonino - Computers in Industry, 2023 - Elsevier
Production flow is becoming increasingly complex since manufacturers must react quickly to
changing markets demands and diverse customer requirements. In order to ensure …

[HTML][HTML] Job shop smart manufacturing scheduling by deep reinforcement learning

JC Serrano-Ruiz, J Mula, R Poler - Journal of Industrial Information …, 2024 - Elsevier
Smart manufacturing scheduling (SMS) requires a high degree of flexibility to successfully
cope with changes in operational decision level planning processes in today's production …

Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

M Wurster, M Michel, MC May, A Kuhnle… - Journal of intelligent …, 2022 - Springer
Remanufacturing includes disassembly and reassembly of used products to save natural
resources and reduce emissions. While assembly is widely understood in the field of …

Reinforcement learning for sustainability enhancement of production lines

A Loffredo, MC May, A Matta, G Lanza - Journal of Intelligent …, 2023 - Springer
The importance of sustainability in industry is dramatically rising in recent years. Controlling
machine states to achieve the best trade-off between production rate and energy demand is …